Updates code snippets in documentation to reference scripts (#1693)

# Description

This change replaces some of the hardcoded python snippets in the sensor
documentation to reference directly from the demo scripts. This avoids
inconsistencies when changes in the scripts or documentations are made.


## Type of change

- This change requires a documentation update

## Checklist

- [X] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [X] I have made corresponding changes to the documentation
- [X] My changes generate no new warnings
- [X] I have added tests that prove my fix is effective or that my
feature works
- [X] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [X] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

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docs/source/overview/sensors/contact_sensor.rst

@@ -14,59 +14,9 @@ By default, the reported force is the total contact force, but your application
 
 Consider a simple environment with an Anymal Quadruped and a block
 
-.. code-block:: python
-
-  @configclass
-  class ContactSensorsSceneCfg(InteractiveSceneCfg):
-      """Design the scene with sensors on the robot."""
-
-      # ground plane
-      ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg())
-
-      # lights
-      dome_light = AssetBaseCfg(
-          prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
-      )
-
-      # robot
-      robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
-
-      # Rigid Object
-      cube = RigidObjectCfg(
-          prim_path="{ENV_REGEX_NS}/Cube",
-          spawn=sim_utils.CuboidCfg(
-              size=(0.5,0.5,0.1),
-              rigid_props=sim_utils.RigidBodyPropertiesCfg(),
-              mass_props=sim_utils.MassPropertiesCfg(mass=100.0),
-              collision_props=sim_utils.CollisionPropertiesCfg(),
-              physics_material=sim_utils.RigidBodyMaterialCfg(static_friction=1.0),
-              visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2),
-          ),
-          init_state=RigidObjectCfg.InitialStateCfg(pos=(0.5, 0.5, 0.05)),
-      )
-
-      contact_forces_LF = ContactSensorCfg(
-          prim_path="{ENV_REGEX_NS}/Robot/LF_FOOT",
-          update_period=0.0,
-          history_length=6,
-          debug_vis=True,
-          filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube"],
-      )
-
-      contact_forces_RF = ContactSensorCfg(
-          prim_path="{ENV_REGEX_NS}/Robot/RF_FOOT",
-          update_period=0.0,
-          history_length=6,
-          debug_vis=True,
-          filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube"],
-      )
-
-      contact_forces_H = ContactSensorCfg(
-          prim_path="{ENV_REGEX_NS}/Robot/.*H_FOOT",
-          update_period=0.0,
-          history_length=6,
-          debug_vis=True,
-      )
+.. literalinclude:: ../../../../source/standalone/demos/sensors/contact_sensor.py
+    :language: python
+    :lines: 40-90
 
 We define the sensors on the feet of the robot in two different ways.  The front feet are independent sensors (one sensor body per foot) and the "Cube" is placed under the left foot.  The hind feet are defined as a single sensor with multiple bodies.
 

docs/source/overview/sensors/frame_transformer.rst

@@ -15,62 +15,9 @@ One of the most common operations that needs to be performed within a physics si
 
 The sensory is minimally defined by a source frame and a list of target frames.  These definitions take the form of a prim path (for the source) and list of regex capable prim paths the rigid bodies to be tracked (for the targets).
 
-.. code-block:: python
-
-  @configclass
-  class FrameTransformerSensorSceneCfg(InteractiveSceneCfg):
-      """Design the scene with sensors on the robot."""
-
-      # ground plane
-      ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg())
-
-      # lights
-      dome_light = AssetBaseCfg(
-          prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
-      )
-
-      # robot
-      robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
-
-      # Rigid Object
-      cube = RigidObjectCfg(
-          prim_path="{ENV_REGEX_NS}/Cube",
-          spawn=sim_utils.CuboidCfg(
-              size=(1,1,1),
-              rigid_props=sim_utils.RigidBodyPropertiesCfg(),
-              mass_props=sim_utils.MassPropertiesCfg(mass=100.0),
-              collision_props=sim_utils.CollisionPropertiesCfg(),
-              physics_material=sim_utils.RigidBodyMaterialCfg(static_friction=1.0),
-              visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0), metallic=0.2),
-          ),
-          init_state=RigidObjectCfg.InitialStateCfg(pos=(5, 0, 0.5)),
-      )
-
-      specific_transforms = FrameTransformerCfg(
-          prim_path="{ENV_REGEX_NS}/Robot/base",
-          target_frames=[
-              FrameTransformerCfg.FrameCfg(prim_path="{ENV_REGEX_NS}/Robot/LF_FOOT"),
-              FrameTransformerCfg.FrameCfg(prim_path="{ENV_REGEX_NS}/Robot/RF_FOOT"),
-          ],
-          debug_vis=True,
-      )
-
-      cube_transform = FrameTransformerCfg(
-          prim_path="{ENV_REGEX_NS}/Robot/base",
-          target_frames=[
-              FrameTransformerCfg.FrameCfg(prim_path="{ENV_REGEX_NS}/Cube")
-          ],
-          debug_vis=False,
-      )
-
-      robot_transforms = FrameTransformerCfg(
-          prim_path="{ENV_REGEX_NS}/Robot/base",
-          target_frames=[
-              FrameTransformerCfg.FrameCfg(prim_path="{ENV_REGEX_NS}/Robot/.*")
-          ],
-          debug_vis=False,
-      )
-
+.. literalinclude:: ../../../../source/standalone/demos/sensors/frame_transformer_sensor.py
+    :language: python
+    :lines: 38-86
 
 We can now run the scene and query the sensor for data
 

docs/source/overview/sensors/ray_caster.rst

@@ -14,43 +14,9 @@ To keep the sensor performant when there are many cloned environments, the line
 
 Using a ray caster sensor requires a **pattern** and a parent xform to be attached to.  The pattern defines how the rays are cast, while the prim properties defines the orientation and position of the sensor (additional offsets can be specified for more exact placement).  Isaac Lab supports a number of ray casting pattern configurations, including a generic LIDAR and grid pattern.
 
-.. code-block:: python
-
-  @configclass
-  class RaycasterSensorSceneCfg(InteractiveSceneCfg):
-      """Design the scene with sensors on the robot."""
-
-      # ground plane with texture for interesting casting results
-      ground = AssetBaseCfg(
-          prim_path="/World/Ground",
-          spawn=sim_utils.UsdFileCfg(
-              usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd",
-              scale = (1,1,1),
-          )
-      )
-
-      # lights
-      dome_light = AssetBaseCfg(
-          prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
-      )
-
-      # robot
-      robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
-
-      ray_caster = RayCasterCfg(
-          prim_path="{ENV_REGEX_NS}/Robot/base/lidar_cage",
-          update_period = 1/60,
-          offset=RayCasterCfg.OffsetCfg(pos=(0,0,0.5)),
-          mesh_prim_paths=["/World/Ground"],
-          attach_yaw_only=True,
-          pattern_cfg=patterns.LidarPatternCfg(
-            channels = 100,
-            vertical_fov_range=[-90, 90],
-            horizontal_fov_range = [-90,90],
-            horizontal_res=1.0),
-          debug_vis=True,
-      )
-
+.. literalinclude:: ../../../../source/standalone/demos/sensors/raycaster_sensor.py
+    :language: python
+    :lines: 40-71
 
 Notice that the units on the pattern config is in degrees! Also, we enable visualization here to explicitly show the pattern in the rendering, but this is not required and should be disabled for performance tuning.
 
@@ -99,7 +65,7 @@ Querying the sensor for data can be done at simulation run time like any other s
 
 Here we can see the data returned by the sensor itself.  Notice first that there are 3 closed brackets at the beginning and the end: this is because the data returned is batched by the number of sensors. The ray cast pattern itself has also been flattened, and so the dimensions of the array are ``[N, B, 3]`` where ``N`` is the number of sensors, ``B`` is the number of cast rays in the pattern, and 3 is the dimension of the casting space. Finally, notice that the first several values in this casting pattern are the same: this is because the lidar pattern is spherical and we have specified our FOV  to be hemispherical, which includes the poles. In this configuration, the "flattening pattern" becomes apparent: the first 180 entries will be the same because it's the bottom pole of this hemisphere, and there will be 180 of them because our horizontal FOV is 180 degrees with a resolution of 1 degree.
 
-You can use this script to experiment with pattern configurations and build an intuition about how the data is stored by altering the ``triggered`` variable on line 99.
+You can use this script to experiment with pattern configurations and build an intuition about how the data is stored by altering the ``triggered`` variable on line 81.
 
 .. dropdown:: Code for raycaster_sensor.py
    :icon: code